Inspired by noise-contrastive estimation (NCE) [1]}, to pull the positive closer and push the negative farther, our self-supervised learning loss is designed as,
\(\mathcal {L}_{\mathrm {ssl}}=-\frac{1}{N} \sum _{v \in \mathcal {V}} \left[\log \sigma (sim^\rho _v) + \log (1-\sigma (sim^\eta _v)) \right]\) 
